Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India

Abrahart, 2008, Neural network estimation of suspended sediment: Potential pitfalls and future directions, 139 Abrahart, 2001, Modelling sediment transfer in Malawi: Comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data sets, 26, 19 American Society of Civil Engineers (ASCE) Task Committee on Application of Artificial Neural Networks in Hydrology, 2000, Artificial neural networks in hydrology. I: Preliminary concepts, Journal of Hydrologic Engineering, 5, 115, 10.1061/(ASCE)1084-0699(2000)5:2(115) American Society of Civil Engineers (ASCE) Task Committee on Application of Artificial Neural Networks in Hydrology, 2000, Artificial neural networks in hydrology. II: Hydrologic applications, Journal of Hydrologic Engineering, 5, 124, 10.1061/(ASCE)1084-0699(2000)5:2(124) Asselman, 2000, Fitting and interpretation of sediment rating curves, Journal of Hydrology, 234, 228, 10.1016/S0022-1694(00)00253-5 Boukhrissa, 2013, Prediction of sediment load by sediment rating curve and neural network (ANN) in El Kebir catchment, Algeria, Journal of Earth System Science, 122, 1303, 10.1007/s12040-013-0347-2 Central Water Commission (CWC), 2012 Chakrapani, 2005, Factors controlling variations in river sediment loads, Current Science, 88, 569 Chang, 2001, A counter propagation fuzzy-neural network modeling approach to real time streamflow prediction, Journal of Hydrology, 245, 153, 10.1016/S0022-1694(01)00350-X Cigizoglu, 2001, Suspended sediment estimation and forecasting using artificial neural networks, Turkish Journal of Engineering and Environmental Sciences, 26, 16 Cigizoglu, 2001, Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves, Turkish Journal of Engineering and Environmental Sciences, 26, 27 Cigizoglu, 2004, Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons, Advances in Water Resources, 27, 185, 10.1016/j.advwatres.2003.10.003 Cigizoglu, 2006, Methods to improve the neural network performance in suspended sediment estimation, Journal of Hydrology, 317, 221, 10.1016/j.jhydrol.2005.05.019 Crowder, 2007, The accuracy of sediment loads when log-transformation produces nonlinear sediment load–discharge relationships, Journal of Hydrology, 336, 250, 10.1016/j.jhydrol.2006.12.024 Daityari, 2017, Temporal and spatial variations in the engineering properties of the sediments in Ramganga River, Ganga Basin, India, Arabian Journal of Geosciences, 10, 134, 10.1007/s12517-017-2915-2 Dawson, 2002, Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China Hydrology and Earth System Sciences Discussions, 6, 619 Dawson, 2001, Hydrological modelling using artificial neural networks, Progress in Physical Geography, 25, 80, 10.1177/030913330102500104 Firat, 2007, River flow estimation using adaptive neuro fuzzy inference system, Mathematics and Computers in Simulation, 75, 87, 10.1016/j.matcom.2006.09.003 Gautam, 2000, Runoff analysis in humid forest catchment with artificial neural network, Journal of Hydrology, 235, 117, 10.1016/S0022-1694(00)00268-7 Ghose, 2018, Sediment yield prediction using neural networks at a watershed in south east India, ISH Journal of Hydraulic Engineering, 24, 230, 10.1080/09715010.2017.1408432 Gupta, 1990, Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment, Himalayas, Engineering Geology, 28, 119, 10.1016/0013-7952(90)90037-2 Hall, M.J., & Minns, A.W. (1993). Rainfall-runoff modeling as a problem in artificial intelligence: Experience with neural network. , In Proceedings of the 4th British hydrological society symposium, (pp. 5. 51–5.57), Cardiff, UK. Hasnain, 1999, Discharge and suspended-sediment concentration of meltwaters, draining from the Dokriani Glacier, Garhwal Himalaya, India, Journal of Hydrology, 218, 191, 10.1016/S0022-1694(99)00033-5 Hassoun, 1995 Hertz, 1991 Himanshu, 2017, Ensemble wavelet-support vector machine approach for prediction of suspended sediment load using hydrometeorological data, Journal of Hydrologic Engineering, 22, 05017006, 10.1061/(ASCE)HE.1943-5584.0001516 Himanshu, 2017, Assessing the applicability of TMPA-3B42V7 precipitation dataset in wavelet-support vector machine approach for suspended sediment load prediction, Journal of Hydrology, 550, 103, 10.1016/j.jhydrol.2017.04.051 Holtschlag, 2001, Optimal estimation of suspended‐sediment concentrations in streams, Hydrological Processes, 15, 1133, 10.1002/hyp.207 Hsu, 1995, Artificial neural network modeling of the rainfall‐runoff process, Water Resources Research, 31, 2517, 10.1029/95WR01955 Jaafar, 2010, Study of water level-discharge relationship using artificial neural network (ANN) in Sungai Gumum, Tasik Chini Pahang Malaysia, Research Journal of Applied Sciences, 5, 20, 10.3923/rjasci.2010.20.26 Jain, 2001, Development of integrated sediment rating curves using ANNs, Journal of Hydraulic Engineering, 127, 30, 10.1061/(ASCE)0733-9429(2001)127:1(30) Kale, 2003, Geomorphic effects of monsoon floods on Indian rivers, Natural Hazards, 28, 65, 10.1023/A:1021121815395 Kaur, 2003, Assessment of a SWAT model for soil and water management in India, Land Use and Water Resources Research, 3, 1 Khan, 1992 Khan, 2019, Spatial variation in the grain size characteristics of sediments in Ramganga River, Ganga Basin, India Khan, 2016, Particle size characteristics of Ramganga catchment area of Ganga River, 307 Khan, 2016, Factors responsible for temporal and spatial variations in water and sediment discharge in Ramganga River, Ganga Basin, India, Environmental Earth Sciences, 75, 1, 10.1007/s12665-015-5148-2 Khan, 2016, Assessment of surface water quality and its spatial variation. A case study of Ramganga River, Ganga Basin, India, Arabian Journal of Geosciences, 9, 28, 10.1007/s12517-015-2134-7 Khan, 2017, Spatial and temporal variations of physicochemical and heavy metal pollution in Ramganga River—A tributary of River Ganges, India, Environmental Earth Sciences, 76, 231, 10.1007/s12665-017-6547-3 Khan, 2016, Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India, Hydrological Sciences Journal, 61, 2084, 10.1080/02626667.2015.1083650 Khan, 2016, Assessment of spatial variations in water quality of Garra River at Shahjahanpur, Ganga Basin, India, Arabian Journal of Geosciences, 9, 1, 10.1007/s12517-016-2551-2 Khan, 2018, Understanding the potential sources and environmental impacts of dissolved and suspended organic carbon in the diversified Ramganga River, Ganges Basin, India, Proceedings of the International Association of Hydrological Sciences, 379, 61, 10.5194/piahs-379-61-2018 Kisi, 2004, River flow modeling using artificial neural networks, Journal of Hydrologic Engineering, 9, 60, 10.1061/(ASCE)1084-0699(2004)9:1(60) Kisi, 2005, Suspended sediment estimation using neuro-fuzzy and neural network approaches, Hydrological Sciences Journal, 50, 683, 10.1623/hysj.2005.50.4.683 Kisi, 2010, Daily suspended sediment estimation using neuro-wavelet models, International Journal of Earth Sciences, 99, 1471, 10.1007/s00531-009-0460-2 Kumar, 2004, ANN applications in hydrology - Merits and demerits, 31 Lagos-Avid, 2017, Predicting the particle size distribution of eroded sediment using artificial neural networks, Science of the Total Environment, 581, 833, 10.1016/j.scitotenv.2017.01.020 Li, 2003, Modeling flow and sediment transport in a river system using an artificial neural network, Environmental Management, 31, 0122, 10.1007/s00267-002-2862-9 Maier, 2000, Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications, Environmental Modelling Software, 15, 101, 10.1016/S1364-8152(99)00007-9 Minns, 1996, Artificial neural networks as rainfall-runoff models, Hydrological Sciences Journal, 41, 399, 10.1080/02626669609491511 Nagy, 2002, Prediction of sediment load concentration in rivers using artificial neural network model, Journal of Hydraulic Engineering, 128, 588, 10.1061/(ASCE)0733-9429(2002)128:6(588) Panwar, 2016, Grain size characteristics and provenance determination of sediment and dissolved load of Alaknanda River, Garhwal Himalaya, India, Environmental Earth Sciences, 75, 91, 10.1007/s12665-015-4785-9 Rai, 2008, Event-based sediment yield modeling using artificial neural network, Water Resources Management, 22, 423, 10.1007/s11269-007-9170-3 Rajaee, 2009, Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models, Science of the Total Environment, 407, 4916, 10.1016/j.scitotenv.2009.05.016 Rajurkar, 2004, Modeling of the daily rainfall-runoff relationship with artificial neural network, Journal of Hydrology, 285, 96, 10.1016/j.jhydrol.2003.08.011 Ray, 1998 Reddy, 2003 Sajikumar, 1999, A non-linear rainfall–runoff model using an artificial neural network, Journal of Hydrology, 216, 32, 10.1016/S0022-1694(98)00273-X Sarangi, 2005, Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India, Agricultural Water Management, 78, 195, 10.1016/j.agwat.2005.02.001 Schmidhuber, 2015, Deep learning in neural networks: An overview, Neural networks, 61, 85, 10.1016/j.neunet.2014.09.003 Shamseldin, 1997, Application of a neural network technique to rainfall-runoff modelling, Journal of Hydrology, 199, 272, 10.1016/S0022-1694(96)03330-6 Shoaib, 2014, Comparative study of different wavelet based neural network models for rainfall–runoff modeling, Journal of Hydrology, 515, 47, 10.1016/j.jhydrol.2014.04.055 Sinnakaudan, 2006, Multiple linear regression model for total bed material load prediction, Journal of Hydraulic Engineering, 132, 521, 10.1061/(ASCE)0733-9429(2006)132:5(521) Smith, 1995, Neural-network models of rainfall-runoff process, Journal of Water Resources Planning and Management, 121, 499, 10.1061/(ASCE)0733-9496(1995)121:6(499) Strahler, 1952, Dynamic basis of geomorphology, Geological Society of America Bulletin, 63, 923, 10.1130/0016-7606(1952)63[923:DBOG]2.0.CO;2 Sudheer, 2005, Knowledge extraction from trained neural network river flow models, Journal of Hydrologic Engineering, 10, 264, 10.1061/(ASCE)1084-0699(2005)10:4(264) Tayyab, 2017, Rainfall-runoff modeling at Jinsha River basin by integrated neural network with discrete wavelet transform, Meteorology and Atmospheric Physics, 1 Tettamanzi, 2001 Tokar, 1999, Rainfall-runoff modeling using artificial neural networks, Journal of Hydrologic Engineering, 4, 232, 10.1061/(ASCE)1084-0699(1999)4:3(232) Tsakiri, 2014, An efficient prediction model for water discharge in Schoharie Creek, NY, Journal of Climatology, 2014, 10.1155/2014/284137 Valdiya, 1998 Walling, 1988, The reliability of rating curve estimates of suspended sediment yield: Some further comments, 337 Wang, 2008, Monitoring event-based suspended sediment concentration by artificial neural network models, WSEAS Transactions on Computers, 5, 359, 10.1109/TC.2007.70821 Wu, 2011, Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis, Journal of Hydrology, 399, 394, 10.1016/j.jhydrol.2011.01.017 Yadav, 2016, Estimation of in-situ bioremediation system cost using a hybrid Extreme Learning Machine (ELM)-particle swarm optimization approach, Journal of Hydrology, 543, 373, 10.1016/j.jhydrol.2016.10.013 Yadav, 2016, Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: A case study in Neckar River, Germany, Measurement, 92, 433, 10.1016/j.measurement.2016.06.042 Yadav, 2018, Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer, Hydrological Sciences Journal, 63, 210, 10.1080/02626667.2017.1413491 Zhang, 2003, Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds, Journal of Hydrology, 273, 18, 10.1016/S0022-1694(02)00313-X